Association rule mining algorithms are generally used to discover all principles in the database fulfilling some base help and least certainty requirements. So as to diminish the quantity of produced rules, the adjustment of the affiliation rule mining algorithm to mine solitary a specific subset of affiliation rules where the characterization class credit is appointed to one side hand-side was examined in past research. In this examination, a dataset about traffic accidents was gathered from Dubai Traffic Department, UAE. After data preprocessing, Apriori and Predictive Apriori affiliation rules algorithms were connected to the dataset so as to investigate the connection between recorded accidents` variables to mishap seriousness in Dubai. Two arrangements of class affiliation rules were created utilizing the two algorithms and condensed to get the most intriguing standards utilizing specialized measures. Exact outcomes demonstrated that the class affiliation rules produced by Apriori algorithm were more compelling than those created by Predictive Apriori algorithm. More relationship between mishap elements and mishap seriousness level were investigated while applying Apriori algorithm.

Association Rule mining, Apriori Algorithm, Road Accident

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